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How Is Federated Learning Implemented on Phoenix Global?

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To improve GPS navigation, MIT researchers are tagging road features on digital maps through Machine learning. Beyond GPS navigation, Machine learning has seen application in many fields ranging from medicine to financial analysis.

Machine learning is constantly evolving because it is a science to educate computers to act like humans in real-life situations. The role of the Internet of Things in a revolutionary society cannot be ignored. The Internet of Things can use advanced machine learning (ML) algorithms for its applications. However, because a large amount of data is stored on a central cloud server, using centralized machine learning algorithms is not a viable option due to huge computational costs and privacy leak issues. 

In this case, blockchain can improve the privacy of IoT networks, allowing them to decentralize without any central authority. However, it remains a challenging task to use sensitive and massive data stored in a distributed manner for application purposes. To overcome this difficult task, Federated Learning (FL) is a new type of ML. This most promising solution can bring learning to end devices without sharing private data with a central server. In simple terms, Federated Learning allows companies to share data in a “closed-loop system.” 

Federated Learning (or Collaborative Learning)

As a fully decentralized machine learning technique, Federated Learning is a step up from the standard centralized and traditional decentralized machine learning techniques. It teaches a shared machine-learning algorithm to different decoupled devices that maintain local data without sharing this data among the devices. 

Through Federated Learning, systems can maintain data privacy, lessen power consumption, decrease waiting time, and create more intelligent algorithms. The Google keyboard GBoard uses the Federated Learning technique.

Phoenix Global

Phoenix Global is the blockchain that hosts next-generation consumer-focused DApps. Phoenix Oracle relays real-world asset prices to the blockchain. PHB, the Phoenix Global cryptocurrency, executes transactions on the blockchain. Phoenix Chain is sidechain-agnostic and bridges with Binance Smart Chain (BSC), Ethereum, Solana, NEO, and Tron. 

Phoenix Global augments Big Data-based Artificial Intelligence (AI) via Federated Learning. Let’s look at this development in closer focus.

Phoenix Global platform focus: Federated Learning

A merger between APEX network and Red Pulse Phoenix gave birth to Phoenix Global. During the Q2 development in 2020, Phoenix Global expressed the desire to work extensively on Federated Learning (FL) use cases and ecosystems. This desire stems from these facts:

  • FL is a blockchain use case bound to scale to collaborate with existing AI capabilities and use cases. 
  • FL can create incremental value as more people participate in the blockchain network.
  • China has a high appetite for AI consumer applications. 

To protect consumer privacy, as more people use data-driven AI applications, the system needs to decentralize Nex-Gen DApps and create AI applications. 

How does the Phoenix Global platform implement Federated Learning?

Phoenix Global, a forerunner in FL use case adoption, has the following strategies to execute FL:

Integration with existing AI systems

FL could integrate with existing machine learning algorithms, such as APEX IQ, to expand FL use cases. APEX Technologies has begun to explore the application of federated learning and blockchain technology to the field of intelligent marketing, aiming to help corporate customers obtain deeper and comprehensive customer insights under the premise of safety and compliance.

Integration with Phoenix Oracle

Integration with the enterprise-ready Phoenix Oracle, which is the proprietary oracle of PHB, will allow quick interaction among AI platforms while maintaining high data security and integrity. By integrating with the enterprise-ready Oracle, FL applications can maximize the value of the blockchain as well as solve certain issues dealing with data integrity, security, anti-cheating, and transparency.

Ecosystem, Network, and Alliance Expansion

Phoenix Global aims to forge more alliance partnerships, incorporating more blockchain networks and ecosystems to amplify FL use cases. For example, the technology giant Green Cloud has partnered with Phoenix Global to probe FL use cases. Phoenix Global is also in partnership with ARPA. The partnership is aimed at implementing the core technology to AI and data computation use cases that requires consumers’ data to remain private. 

Team Expansion

In order to further realize the future of FL on the blockchain, PHB will provide big data analysis to any entity that wishes to utilize it at the fraction of the current cost, Phoenix Global is already hiring engineers and developers to expand its  FL team. Transparency and verification of the data on blockchain will help hurdle blockages, no matter their specific industries.

Conclusion

For the futuristic development of AI-based consumer applications, Phoenix Global integrates Federated Learning with blockchain technology. Since May 2021, the company has been making strategic moves to achieve this integration to scale it with existing AI systems.

PHB also promotes community participation in creating marketing channels and adding new partners. Its latest development endeavor is focused on onboarding Korean and Chinese partners and communities with roots in AI-based technology. AI and blockchain technology are innovations ahead of their time. However, they are arguably in their infancy, and their futures hinge on decisions made today. Phoenix Global is creating a community dedicated to a common goal – giving both technologies the best possible outcome. PHB’s interest in Federated Learning portrays its dedication to making this vision a reality.

Disclaimer. The information provided is not trading advice. Cryptopolitan.com holds no liability for any investments made based on the information provided on this page. We strongly recommend independent research and/or consultation with a qualified professional before making any investment decisions.

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